Test bench performance attenuation evaluation method, system and equipment based on defect detection

Abstract

The invention relates to an aero-engine test bed structure health monitoring and performance evaluation technology, and provides a test bed performance attenuation evaluation method, system and equipment based on defect detection, and the method comprises the steps: building a finite element numerical model of a test bed based on at least two types of non-destructive detection methods; performing order reduction and solution on the finite element numerical model through a singular value decomposition method to obtain an order reduction model; and acquiring the predicted structural displacement of the test bench in the reduced-order model, acquiring the actual structural displacement of the test bench under the test load, and evaluating the structural performance attenuation of the test bench through the predicted structural displacement and the actual structural displacement. According to the method, on-line structure health identification and performance early warning can be carried out on the test bed through the constructed degradation model in combination with real-time monitoring data, and the method has good accuracy, expandability and engineering practical value and can be widely applied to state evaluation and fault prediction of the aero-engine test bed.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of test equipment health management, and relates to a structural health monitoring and performance evaluation technology of an aero-engine test bench, in particular to a test bench performance attenuation evaluation method, system and equipment based on defect detection. BACKGROUND

[0002] The aero-engine test bench is applied to extreme environment simulation experiments, and long-term operation of the aero-engine test bench may lead to performance attenuation and even local structure failure due to factors such as fatigue, vibration and high temperature. Traditional performance evaluation methods mostly rely on manual inspection or regular disassembly and inspection, which is low in efficiency, large in error and difficult to reflect structural hidden dangers in time.

[0003] In recent years, structural defect detection technologies such as ultrasonic, infrared thermal imaging, acoustic emission and visual detection have made great progress, providing more abundant basis for structural state evaluation. However, how to effectively associate defect information with performance degradation rules through a reduced numerical model to realize quantitative performance evaluation of the test bench in the whole life cycle is still a technical difficulty at present. SUMMARY

[0004] In order to solve the technical problem of how to effectively associate defect information with performance degradation rules through a reduced numerical model to realize quantitative performance evaluation of the test bench in the whole life cycle, the present application discloses a test bench performance attenuation evaluation method based on defect detection, which comprises the following steps: S1, constructing a finite element numerical model of the test bench based on at least two types of non-destructive detection methods; S2, generating a displacement snapshot matrix through the finite element numerical model, and obtaining a reduced basis matrix through singular value decomposition and reduction of the displacement snapshot matrix; S3, establishing an initial reduced model using the reduced basis matrix, and solving the initial reduced model to obtain a reduced model using a designed load and a stiffness matrix; S4, inputting a test load into the reduced model to obtain a predicted structural displacement of the test bench, collecting an actual structural displacement of the test bench under the test load, and performing structural performance attenuation evaluation of the test bench through the predicted structural displacement and the actual structural displacement.

[0005] Further, in step S1, the finite element numerical model of the test bench is constructed based on at least two types of non-destructive detection methods, which comprises: S11, selecting at least two types of non-destructive detection methods to detect the test bench and obtain a defect data set, modeling and fusing all the defect data to obtain a defect description vector, the non-destructive detection methods including at least two of ultrasonic detection, eddy current detection, three-dimensional laser scanning and X-ray detection; S12, an initial finite element numerical model is constructed with material performance parameters and design load as input and bench structure displacement as output, and the initial finite element numerical model is trained by a defect description vector to obtain a trained finite element numerical model.

[0006] Further, in step S11, all the defect data are standardized and fused by a unified spatial coordinate and feature extraction method to obtain defect description vectors with unified format, and the defect description vectors include defect positions and defect geometric parameters.

[0007] Further, in step S2, a displacement snapshot matrix is generated by the finite element numerical model, and singular value decomposition and order reduction are performed on the displacement snapshot matrix to obtain a reduced basis matrix, including: S21, structure displacements under multiple load conditions are obtained by the finite element numerical model to generate a displacement snapshot matrix; S22, singular value decomposition is performed on the displacement snapshot matrix to obtain a basis matrix and a singular value matrix; S23, a plurality of singular value vectors with energy greater than an energy threshold are selected from the singular value matrix, and a feature vector corresponding to each singular value vector is extracted from the basis matrix to generate a reduced basis matrix.

[0008] Further, in step S3, an initial reduced model is established by using the reduced basis matrix, and a final reduced model is obtained by solving the initial reduced model with design load and stiffness matrix, including: S31, the expression of the initial reduced model is , wherein, is the reduced basis matrix, is the predicted structure displacement, and a is the reduced model weight coefficient; S32, the stiffness matrix is obtained according to the material performance parameters of the test bench, the structure displacement under the design load is obtained by the stiffness matrix, the reduced model weight coefficient is solved by the structure displacement and the reduced basis matrix, and the reduced model is obtained.

[0009] Further, in step S3, it further includes: S33, the structure displacement under a given load is obtained by the finite element numerical model and the reduced model respectively, the error between the two structure displacements is calculated, the reduced model weight coefficient is iteratively corrected according to the error and an error threshold, until a new reduced model weight coefficient satisfying the error being less than the error threshold is obtained, and a final reduced model is obtained.

[0010] Further, in step S4, a residual error between the predicted structural displacement and the actual structural displacement is calculated, and the residual error is taken as a structural performance degradation index, and if the residual error is greater than or equal to a residual error threshold, it is determined that the structural performance of the test bench is degraded.

[0011] In an improved embodiment of the test bench performance degradation evaluation method based on defect detection, the method further comprises: S5, according to the predicted structural displacement, using the Miner linear cumulative damage method or the Coffin-Manson model to identify the shortest path and the potential failure position of the test bench.

[0012] The embodiment of the present application also provides a test bench performance degradation evaluation system based on defect detection, comprising a first construction module, a reduced-order basis matrix acquisition module, a second construction module and a structural performance degradation evaluation module.

[0013] The first construction module is configured to construct a finite element numerical model of the test bench based on at least two types of non-destructive detection methods. The reduced-order basis matrix acquisition module is configured to generate a displacement snapshot matrix from the finite element numerical model, and perform singular value decomposition and reduction on the displacement snapshot matrix to obtain a reduced-order basis matrix. The second construction module is configured to establish an initial reduced-order model using the reduced-order basis matrix, and solve the initial reduced-order model using a design load and a stiffness matrix to obtain a reduced-order model. The structural performance degradation evaluation module is configured to input a test load into the reduced-order model to obtain a predicted structural displacement of the test bench, collect an actual structural displacement of the test bench under the test load, and perform structural performance degradation evaluation of the test bench based on the predicted structural displacement and the actual structural displacement.

[0014] The embodiment of the present application also provides a computer device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the above-mentioned any test bench performance degradation evaluation method based on defect detection is realized, so as to solve the technical problem of how to effectively associate defect information with performance degradation law through a reduced-order numerical model, and realize quantitative performance evaluation of the test bench in the whole life cycle.

[0015] Compared with the prior art, the above-mentioned at least one technical solution adopted by the embodiments of the present application can achieve the beneficial effects at least including: 1. Multi-source defect data fusion improves evaluation accuracy: Through the joint modeling of multi-source heterogeneous detection information, the comprehensiveness and accuracy of defect identification and performance evaluation are improved. Compared with the diagnosis method of a single sensor, multi-sensor data fusion can take advantage of each detection technology, expand the depth and breadth of structure state perception, reduce the misjudgment probability caused by single data deviation, and more reliably quantify the health status of the test bench.

[0016] 2. Reduced order model accelerates simulation to support rapid evaluation: By using singular value decomposition-based measurement, a reduced order model is obtained from the finite element numerical model, which greatly reduces the dimension of the model. Through the reduced order model, the structural performance under multiple working conditions can be rapidly evaluated. Moreover, under the premise of ensuring prediction accuracy, the reduced order model can improve the computational efficiency by several orders of magnitude, and can realize real-time iterative analysis under different defect conditions or load conditions, meeting the rapidity requirements of test bench health monitoring and life prediction. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor under the premise of the drawings.

[0018] Figure 1 The flowchart of the test bench performance degradation evaluation method based on defect detection of the present application; Figure 2 The architecture of the test bench performance degradation evaluation system based on defect detection of the present application; Among them, 201, the first construction module; 202, the reduced order basis matrix acquisition module; 203, the second construction module; 204, the structural performance degradation evaluation module. DETAILED DESCRIPTION

[0019] The embodiments of the present application will be described in detail below with reference to the drawings.

[0020] Following, the embodiments of the present application are described through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the disclosure. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. The present application can also be implemented or applied by other different specific embodiments, and various modifications or changes can be made to the details in the specification without departing from the spirit of the present application. It should be noted that the following embodiments and features of the embodiments can be combined with each other without conflict. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0021] The embodiments of the present application disclose a test bench performance degradation evaluation method based on defect detection, as shown in Figure 1 The method comprises the following steps: S1, constructing a finite element numerical model of the test bench based on at least two types of non-destructive detection methods; S2, generating a displacement snapshot matrix through the finite element numerical model, and obtaining a reduced basis matrix through singular value decomposition and reduction of the displacement snapshot matrix; S3, establishing an initial reduced model using the reduced basis matrix, and solving the initial reduced model using a design load and a stiffness matrix to obtain a reduced model; S4, inputting a test load into the reduced model to obtain a predicted structural displacement of the test bench, collecting an actual structural displacement of the test bench under the test load, and performing structural performance degradation evaluation of the test bench through the predicted structural displacement and the actual structural displacement.

[0022] Further, in step S1, the finite element numerical model of the test bench is constructed based on at least two types of non-destructive detection methods, comprising: S11, selecting at least two types of non-destructive detection methods to detect the test bench and obtain a defect data set, and modeling and fusing all the defect data to obtain a defect description vector.

[0023] The non-destructive detection method comprises at least two of ultrasonic detection, eddy current detection, three-dimensional laser scanning and X-ray detection.

[0024] The ultrasonic detection method is used to identify cracks, cavities or delamination defects inside the material.

[0025] The eddy current detection is used to identify cracks and corrosion on the surface or near the surface of the conductive metal member.

[0026] The three-dimensional laser scanning method is used to collect the geometric appearance and deformation characteristics of the structure surface, and is used to assist in constructing a defect space distribution model.

[0027] X-ray detection method is used for imaging identification of internal defects of key metal components or welding positions of the test bench. Through the above methods, defects such as cracks, pores, inclusions and incomplete penetration on the test bench can be identified.

[0028] For the support part of the test bench which cannot be disassembled, the welding joint or reinforcement part therein can be subjected to fine internal defect detection by using digital radiography (DR) or computed tomography (CT) to improve the comprehensiveness and accuracy of the evaluation.

[0029] In the present application, the defect data sets obtained by selecting at least two non-destructive testing methods are subjected to joint analysis by a multi-source fusion data processing method to obtain a defect description vector, so as to improve the accuracy and robustness of defect identification. In implementation, the data of different non-destructive testing methods can be uniformly modeled, and respective defect descriptions are constructed. Each defect description is standardized according to a unified spatial reference system and feature extraction method, and a defect description vector in a unified format is constructed. The defect description vector includes spatial position coordinates (x, y, z) and defect geometric parameters (such as length, width, depth, volume), and can also include the following fields: spatial position coordinates (x, y, z); defect geometric parameters (such as length, width, depth, volume); physical response indicators (such as echo amplitude, impedance phase change, heat flow gradient); image texture features (such as gray moment, LBP feature, Gabor texture); detection method code (identifies data source: UT, ET, IRT, 3D, X-ray); confidence score (probability output from classification model).

[0030] S12, taking material performance parameters and design load as input and taking test bench structure displacement as output, an initial finite element numerical model is constructed, and the initial finite element numerical model is trained by the defect description vector to obtain a trained finite element numerical model.

[0031] Further, in step S2, a displacement snapshot matrix is generated by the finite element numerical model, and singular value decomposition and order reduction are performed on the displacement snapshot matrix to obtain a reduced basis matrix, including: S21, obtaining structure displacement under multiple load conditions by the finite element numerical model, and generating a displacement snapshot matrix; S22, singular value decomposition is performed on the displacement snapshot matrix to obtain a basis matrix and a singular value matrix; S23, a plurality of singular value vectors with energy greater than an energy threshold are selected from the singular value matrix, and a feature vector corresponding to each singular value vector is extracted from the basis matrix to generate a reduced-order basis matrix.

[0032] In implementation, the structure displacement under n load conditions can be obtained by the finite element numerical model established in step S1, and can be respectively expressed as The structure displacement under n load conditions can form a displacement snapshot matrix S as shown below: ; The displacement snapshot matrix S is a real matrix with m rows and n columns, where m is the number of degrees of freedom, and n is the number of load conditions. Singular value decomposition is performed on the displacement snapshot matrix S to obtain U, V, and three matrices. , wherein U is a basis matrix of m order, V is a unitary matrix of n order, is the complex conjugate of V, is a singular value matrix with m rows and n columns.

[0033] The present application retains the first r singular value vectors with energy greater than the energy threshold from the singular value matrix, and then extracts the feature vector corresponding to each singular value vector in the basis matrix U, to obtain a reduced-order basis matrix including r feature vectors .

[0034] Further, in step S3, an initial reduced-order model is established using the reduced-order basis matrix, and a final reduced-order model is obtained by solving the initial reduced-order model using the design load and the stiffness matrix, including: S31, the expression of the initial reduced-order model is , wherein, is a reduced-order basis matrix, is a predicted structure displacement, and a is a reduced-order model weight coefficient; S32, the stiffness matrix is obtained according to the material performance parameters of the test bench, the structure displacement under the design load is obtained through the stiffness matrix K, the reduced-order model weight coefficient is solved through the structure displacement and the reduced-order basis matrix, and the reduced-order model is obtained.

[0035] Further, in step S3, it further includes: S33, respectively through the finite element numerical model and the reduced order model, obtain the structure displacement under a given load, calculate the error between the two structure displacements, and iteratively correct the reduced order model weight coefficient according to the error and an error threshold value until a new reduced order model weight coefficient that satisfies the error being less than the error threshold value is obtained, and a final reduced order model is obtained.

[0036] In a specific implementation, among the two structure displacements, the structure displacement output by the finite element numerical model can be represented as , the predicted structure displacement output by the reduced order model is , and the error threshold value can be set to 5.0%. When the error between the two structure displacements satisfies , it indicates that the prediction accuracy of the reduced order model is better. If the above condition is not satisfied, a new reduced order basis matrix can be calculated by adjusting the energy threshold value in step S23, and then the reduced order model weight coefficient is recalculated based on the new reduced order basis matrix.

[0037] Further, the health state recognition is based on the deviation between the prediction result of the reduced order model and the actual measurement result to perform damage inversion and state estimation. Therefore, in step S4, the residual error between the predicted structure displacement and the actual structure displacement is calculated, the residual error is taken as a structural functional degradation index, and if the residual error is greater than or equal to a residual error threshold value, it is determined that the structural performance of the test bench is attenuated. Wherein, the residual error R can be calculated by the formula , wherein, is the actual structure displacement. In implementation, a level threshold value for dividing the structural functional degradation index can also be set to divide the structural performance attenuation of the test bench into different levels.

[0038] In an improved embodiment of the above test bench performance attenuation evaluation method based on defect detection, the method further comprises: S5, according to the predicted structure displacement, a Miner linear cumulative damage method or a Coffin-Manson model is used to identify the shortest path of the service life and the potential failure position on the test bench.

[0039] Based on the same inventive concept, the embodiment of the present application also provides a test bench performance degradation evaluation system based on defect detection, as described in the following embodiment. Since the principle of solving the problem of the test bench performance degradation evaluation system based on defect detection is similar to the test bench performance degradation evaluation method based on defect detection disclosed in the above embodiment, the implementation of the test bench performance degradation evaluation system based on defect detection can be referred to the implementation of the test bench performance degradation evaluation method based on defect detection, and the repeated parts will not be described here. The term "unit" or "module" used below can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiment is preferably implemented in software, hardware, or a combination of software and hardware is also possible and is conceived.

[0040] Figure 2 is a structural block diagram of the test bench performance degradation evaluation system based on defect detection disclosed in the embodiment of the present application, as Figure 2 shown, the system comprises a first construction module 201, a reduced-order basis matrix acquisition module 202, a second construction module 203 and a structural performance degradation evaluation module 204, which will be described below.

[0041] The first construction module 201 is used to construct a finite element numerical model of the test bench based on at least two types of non-destructive detection methods. The reduced-order basis matrix acquisition module 202 acquires a displacement snapshot matrix generated by the finite element numerical model, and performs singular value decomposition and reduction on the displacement snapshot matrix to obtain a reduced-order basis matrix. The second construction module 203 is used to establish an initial reduced-order model using the reduced-order basis matrix, and solve the initial reduced-order model using a design load and a stiffness matrix to obtain a reduced-order model. The structural performance degradation evaluation module 204 is used to input a test load into the reduced-order model to obtain a predicted structural displacement of the test bench, collect an actual structural displacement of the test bench under the test load, and perform structural performance degradation evaluation of the test bench through the predicted structural displacement and the actual structural displacement.

[0042] The embodiment of the present application achieves the following technical effects: 1. Multi-source defect data fusion, improving evaluation accuracy: Through the joint modeling of multi-source heterogeneous detection information, the comprehensiveness and accuracy of defect identification and performance evaluation are improved. Compared with the diagnosis method of a single sensor, multi-sensor data fusion can take advantage of each detection technology, expand the depth and breadth of structure state perception, and reduce the probability of misjudgment caused by single data deviation, so as to more reliably quantify the health status of the test bench.

[0043] 2. Reduced order model accelerates simulation and supports rapid evaluation: By using the singular value decomposition based measurement, a reduced order model is obtained from the finite element numerical model, which greatly reduces the dimension of the model. Through the reduced order model, the rapid evaluation of the structural performance under multiple working conditions can be realized. Moreover, under the premise of ensuring the prediction accuracy, the reduced order model can improve the calculation efficiency by several orders of magnitude, and can realize real-time iterative analysis under different defect conditions or load conditions, so as to meet the rapidity requirements of the test bench health monitoring and life prediction.

[0044] In the embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements any of the above-mentioned defect detection based test bench performance degradation evaluation methods when executing the computer program.

[0045] Specifically, the computer device can be a computer terminal, a server or a similar computing device.

[0046] In the embodiment, a computer readable storage medium is provided, which stores a computer program for executing any of the above-mentioned defect detection based test bench performance degradation evaluation methods.

[0047] Specifically, the computer readable storage medium includes permanent and non-permanent, removable and non-removable media, which can be realized by any method or technology to store information. The information can be computer readable instructions, data structures, program modules or other data. Examples of computer readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other memory technologies, compact disc read only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. According to the definition in this paper, the computer readable storage medium does not include transitory computer readable medium, such as modulated data signal and carrier wave.

[0048] Obviously, those skilled in the art should understand that each module or each step of the above-mentioned embodiments of the present application can be realized by a general computing device, which can be centralized on a single computing device or distributed on a network composed of multiple computing devices, and optionally, each module or each step can be realized by program codes executable by a computing device, so that each module or each step can be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described can be executed in different orders, or each module can be manufactured as an individual integrated circuit module, or multiple modules or steps can be manufactured as a single integrated circuit module. Therefore, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0049] The above only describes the preferred embodiments of the present application and is not intended to limit the present application. The embodiments of the present application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for evaluating the performance degradation of a test bench based on defect detection, characterized in that, include: A finite element numerical model of the test bench was constructed based on at least two types of non-destructive testing methods; A displacement snapshot matrix is ​​generated using the finite element numerical model, and a reduced-order basis matrix is ​​obtained by performing singular value decomposition and order reduction on the displacement snapshot matrix. An initial reduced-order model is established using the reduced-order basis matrix, and the reduced-order model is obtained by solving the initial reduced-order model using the design load and stiffness matrix; The test load is input into the reduced-order model to obtain the predicted structural displacement of the test bench, and the actual structural displacement of the test bench under the test load is collected. The structural performance degradation of the test bench is evaluated by using the predicted structural displacement and the actual structural displacement.

2. The method for evaluating the performance degradation of test benches based on defect detection according to claim 1, characterized in that, A finite element numerical model of the test bench was constructed based on at least two types of non-destructive testing methods, including: At least two types of non-destructive testing methods are selected to inspect the test bench and obtain a defect dataset. All the defect data are modeled and fused to obtain a defect description vector. The non-destructive testing methods include at least two of ultrasonic testing, eddy current testing, three-dimensional laser scanning and X-ray testing. Using material performance parameters and design loads as inputs and bench structure displacement as output, an initial finite element numerical model is constructed. The initial finite element numerical model is then trained using defect description vectors to obtain a trained finite element numerical model.

3. The method for evaluating the performance degradation of test benches based on defect detection according to claim 2, characterized in that, By using a unified spatial coordinate and feature extraction method, all the defect data are standardized and fused to obtain a defect description vector with a unified format, which includes the defect location and defect geometric parameters.

4. The method for evaluating the performance degradation of test benches based on defect detection according to claim 1, characterized in that, A displacement snapshot matrix is ​​generated using the finite element numerical model. Singular value decomposition and order reduction are then performed on the displacement snapshot matrix to obtain a reduced-order basis matrix, including: The structural displacements under multiple load conditions are obtained through the finite element numerical model, and a displacement snapshot matrix is ​​generated. The singular value decomposition of the displacement snapshot matrix yields the basis matrix and the singular value matrix; Multiple singular value vectors with energies greater than an energy threshold are selected from the singular value matrix, and eigenvectors corresponding to each singular value vector are extracted from the basis matrix to generate a reduced-order basis matrix.

5. The method for evaluating the performance degradation of test benches based on defect detection according to claim 1, characterized in that, An initial reduced-order model is established using the aforementioned reduced-order basis matrix. The final reduced-order model is obtained by solving the initial reduced-order model using the design load and stiffness matrix, including: The expression for the initial order reduction model is: ,in, For a reduced-order basis matrix, To predict structural displacement, 'a' is the weighting coefficient of the reduced-order model; The stiffness matrix is ​​obtained based on the material performance parameters of the test bench. The structural displacement under the design load is obtained through the stiffness matrix. The weight coefficients of the reduced-order model are solved through the structural displacement and the reduced-order basis matrix to obtain the reduced-order model.

6. The method for evaluating the performance degradation of a test bench based on defect detection according to claim 1 or 5, characterized in that, Also includes: The structural displacement under a given load is obtained through the finite element numerical model and the reduced-order model, respectively. The error between the two structural displacements is calculated. The weight coefficients of the reduced-order model are iteratively corrected according to the error and the error threshold until a new reduced-order model weight coefficient is obtained that satisfies the error being less than the error threshold, thus obtaining the final reduced-order model.

7. The method for evaluating the performance degradation of test benches based on defect detection according to claim 1, characterized in that, Calculate the residual between the predicted structural displacement and the actual structural displacement, and use the residual as an indicator of structural functional degradation. If the residual is greater than or equal to a residual threshold, the structural performance of the test bench is judged to have degraded.

8. The method for evaluating the performance degradation of test benches based on defect detection according to claim 1, characterized in that, Also includes: Based on the predicted structural displacement, the shortest life path and potential failure location on the test bench are identified using the Miner linear cumulative damage method or the Coffin-Manson model.

9. A test bench performance degradation evaluation system based on defect detection, characterized in that, include: A first construction module is used to construct a finite element numerical model of a test bench based on at least two types of non-destructive testing methods. A reduced-order basis matrix acquisition module is used to generate a displacement snapshot matrix through the finite element numerical model, and to obtain a reduced-order basis matrix by performing singular value decomposition and order reduction on the displacement snapshot matrix. The second construction module is used to establish an initial reduced-order model using the reduced-order basis matrix, and to solve the initial reduced-order model using the design load and stiffness matrix to obtain the reduced-order model. The structural performance degradation assessment module is used to input the test load into the reduced-order model to obtain the predicted structural displacement of the test bench, collect the actual structural displacement of the test bench under the test load, and evaluate the structural performance degradation of the test bench through the predicted structural displacement and the actual structural displacement.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the test bench performance degradation evaluation method based on defect detection as described in any one of claims 1 to 8.

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